The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions

Elazab, Abdelhalim; Bort, J.; Zhou, Bangwei; Serret, María Dolores; Nieto‐Taladriz, M. T.; Araus, José Luís

doi:10.1016/j.agwat.2015.05.003

Cited by 35 publications

(30 citation statements)

References 78 publications

(200 reference statements)

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“…Therefore, the NDVI values remained almost unchanged as GY increased from 4 to 13 Mg ha −1 . These results support the previously reported saturation of reflectance spectra in the red and near-infrared regions, such that increasing leaf area does not involve a parallel increase in NDVI values (Hobbs, 1995 ; Elazab et al, 2015 ). Thus, the relationship between NDVI and aerial biomass saturates as canopies become denser (i.e., LAI > 4) and as a consequence the relationship between the NDVI and GY also worsened as GY increased.…”

Section: Discussionsupporting

confidence: 92%

“…As previously found in other studies in wheat grown under different stress conditions (Casadesus et al, 2007 ; Morgounov et al, 2014 ; Vergara-Diaz et al, 2015 ), the RGB canopy indices (from BreedPix software) measured at flowering were strongly correlated with GY. RGB-based indices may perform far better than NDVI for GY prediction, which has been recently described under water and biotic stresses in wheat (Elazab et al, 2015 ; Vergara-Diaz et al, 2015 ; Zhou et al, 2015 ). The lower accuracy of NDVI in comparison to digital-based RGB indices can be explained in several ways.…”

Section: Discussionmentioning

confidence: 93%

“…For its part, the range of variability in the RGB canopy index, GA, was much wider (only 63% of values were in the range of 0.5–0.8) and GA values in the low N treatments were somewhat smaller (average GA = 0.46 in the 0N treatment) than those of the NDVI, and in fact GY correlated much better with GA than with the NDVI. Even so, the RGB canopy indices also seemed to saturate for high GY but to a lesser extent than the NDVI because they mainly depend on changes in pigment concentration and the canopy LAI is less affected in the visible region than in the NIR region (Casadesus et al, 2007 ; Elazab et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

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A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Vergara-Díaz

Zaman-Allah²,

Masuka³

et al. 2016

Front. Plant Sci.

118

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Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R2~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

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A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Vergara-Díaz

Zaman-Allah²,

Masuka³

et al. 2016

Front. Plant Sci.

118

View full text Add to dashboard Cite

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“…More importantly, scheduling irrigation at anthesis may be fully relevant for cereals under Mediterranean conditions where drought increases progressively during the reproductive stage of the crop. Thus, following previous reports in wheat [54][55][56], a significant association between the NDVI, GA and GGA vegetation indices and the water status parameters (δ 13 C, g s and CTD) was also found in this study. The NDVI, GA and GGA were positively associated with CTD and g s and negatively associated with δ 13 C. In this context, Lopes and Reynolds (2012) [57] reported that the relationship observed between chlorophyll retention or 'stay-green' (assessed via the NDVI) and canopy temperature would confirm the functionality of stay-green in terms of gas exchange and would explain a better capacity to use water by the stay-green genotypes under stressful environments related to low fertilization and the lack of water.…”

Section: Discussionsupporting

confidence: 91%

“…The GA vegetation index was chosen by the model as the first independent variable, explaining 66% of GY variability under SI-2 without N fertilizer. Moreover, various studies have reported that RGB-based indices may perform far better than the NDVI for GY prediction in wheat [32,56,65]. In our study, the NDVI failed to assess GY under irrigation.…”

Section: Relationship Of Vegetation Indices and Water Status Traits Wcontrasting

confidence: 61%

Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

et al. 2019

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Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more efficient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under different water and nitrogen regimes in north Algeria. Differences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY.

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Interactive effect of water and nitrogen regimes on plant growth, root traits and water status of old and modern durum wheat genotypes

Elazab

Serret

Araus

2016

Planta

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The selection of the ideal root drought adaptive traits should take into account the production and maintenance of root tissues alongside the capacity to capture soil resources. Ten old and modern Spanish durum wheat (Triticum turgidum L. var durum) genotypes were grown in lysimeters under two contrasting water and nitrogen regimes to study the effect of such growth conditions on: (1) the aerial biomass, (2) the growth and structure of the roots and (3) the relationships of the root structure with aerial biomass, photosynthetic and transpirative characteristics and water use efficiency. Both high water and nitrogen regimes significantly increased aerial biomass. Root dry biomass and root length increased and decreased in response to improved water supply and nitrogen regimes, respectively. No significant correlations were detected between aerial biomass and any root trait under well-watered conditions. Under water stress aerial biomass was negatively correlated with root dry biomass, root length and root weight density and positively correlated with the specific root length, particularly for the subset of old genotypes. The high nitrogen regime significantly enriched the carbon isotope composition of the flag leaf (δ (13)CFL) and hindered the effect of the high water regime on decreasing δ (13)CFL enrichment. Thus, positive correlations of aerial biomass with δ (13)CFL were detected regardless of the water regime. The study revealed: (1) the importance of root traits for higher aerial biomass under the low water regime; (2) that the interaction between nitrogen and the water regime may affect the predictive nature of the δ (13)C in drought breeding programs; and (3) the selection of the ideal root system structure should take into account the metabolic costs of the production and maintenance of root tissues alongside the capacity of capturing resources.

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The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions

Cited by 35 publications

References 78 publications

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

Interactive effect of water and nitrogen regimes on plant growth, root traits and water status of old and modern durum wheat genotypes

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